Expectation-Maximization χ Self-Organizing Maps for Image classification

Thales Sehn Korting, Leila Maria Garcia Fonseca, Fernando Bação

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

To deal with the huge volume of information provided by remote sensing satellites, which produce images used for agriculture monitoring, urban planning, deforestation detection and so on, several algorithms for image classification have been proposed in the literature. This article compares two approaches, called Expectation-Maximization (EM) and Self-Organizing Maps (SOM) applied to unsupervised image classification, i.e. data clustering without direct intervention of specialist guidance. Remote sensing images are used to test both algorithms, and results are shown concerning visual quality, matching rate and processing time.

Original languageEnglish
Title of host publicationSITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems
Pages359-365
Number of pages7
DOIs
Publication statusPublished - 2008
Event4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008 - Bali, Indonesia
Duration: 30 Nov 20083 Dec 2008

Conference

Conference4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008
CountryIndonesia
CityBali
Period30/11/083/12/08

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